A Distributed Acoustic Sensor System for Intelligent Transportation using Deep Learning

TL;DR

This paper introduces a deep learning approach to analyze optical fibre-based distributed acoustic sensor data for vehicle classification and occupancy detection in intelligent transportation systems, offering a non-intrusive, scalable alternative to traditional methods.

Contribution

The work presents a novel deep learning method for processing DAS signals to classify vehicles and estimate occupancy, improving scalability and privacy compared to existing techniques.

Findings

92% vehicle classification accuracy

92-97% occupancy detection accuracy

Effective non-intrusive traffic analysis

Abstract

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras. This work explores a novel data source based on optical fibre-based distributed acoustic sensors (DAS) for traffic analysis. Detecting the type of vehicle and estimating the occupancy of vehicles are prime concerns in ITS. The first is motivated by the need for tracking, controlling, and forecasting traffic flow. The second targets the regulation of high occupancy vehicle lanes in an attempt to reduce emissions and congestion. These tasks are often conducted by individuals inspecting vehicles or through the use of emerging computer vision technologies. The former is not scale-able nor efficient whereas the latter is intrusive to passengers' privacy. To this end, we propose a deep learning technique to analyse DAS signals to address this challenge through continuous sensing and without exposing personal information. We propose a deep learning method for processing DAS signals and achieve 92% vehicle classification accuracy and 92-97% in occupancy detection based on DAS data collected under controlled conditions.